Using State and Federal Data to Analyze and Model State Health Markets: Examples and Lessons Learned

Scott LeitzDirector, Health Economics ProgramMinnesota Department of Health

November 10, 2004

Overview

Some background on state and federal data sources for analysis and modeling

A few examples of Minnesota modeling exercises

Lessons learned and things to consider

State versus Federal data sources for analysis and modeling

State legislators generally believe their state is unique – Not having state data can be a reason not to do

something, therefore collection of state-specific information is critical

But: not every question asked by state policymakers can be answered with state-specific data

Even when it can, the estimates can sometimes differ– Example: CPS versus state-specific surveys

State versus Federal data sources for analysis and modeling (II)

Even where state data may not be available, or is limited, national data can be used and adjustments made– Assumptions are important

National data is a good crosscheck to state data

Example 1

How much uncompensated care might result from a proposal to eliminate a state health insurance program for very low income people and reduce income eligibility for a Medicaid population?

The Challenge

Turning estimates of enrollment loss into hospital-specific estimates of uncompensated care

Multiple steps involved:– How many will end up without coverage? – How many services will this population seek? – How will that care get paid for?– How will behavior change?

Need for using both state and national data to answer these questions

A brief overview of methodology

Estimated number of people who would lose coverage under Governor’s proposal, adjusted for take-up (crowd out studies)

Adjust result to account for differences in expenditures between the uninsured and the insured:– Uninsured spend approximately half of what the insured

spend on health care. (MEPS, Hadley & Holahan 2003, Long & Marquis 1994).

– Adjustment to reflect that public program enrollees are sicker in general than the uninsured (2001 MN Health Access Survey, Holahan 2001).

– Result: estimate uninsured spend 61% of what they would have spent if enrolled in a public program.

Methodology (II)

Resulting figure is the estimated use of services by the additional uninsured (“uninsured costs”).

Uninsured costs can be “paid” for in two ways:– Out of pocket payments by the uninsured – Uncompensated care

Research shows that the uninsured pay around a third of their health care costs – Surprisingly consistent across income levels– (MEPS, Hadley & Holahan 2003).

Remaining is uncompensated care

Methodology (III)

This uncompensated care figure is divided between hospital-based uncompensated care and clinic-based uncompensated care.

UC allocated 34% to clinics and 66% to hospitals (Hadley & Holahan 2003, 2000 Minnesota-specific analysis of uncompensated care).

Results: Estimated Impact on the Uninsurance Rate

Percentage of Minnesotans without health coverage increases by the following relative to current levels, assuming all other things remain constant:– Baseline, 2002: 5.4%– 2004: 6.0% – 2005: 6.4% – 2006: 6.5% – 2007: 6.6%

Additional of approximately 63,000 additonal uninsured Minnesotans

$0

$40,000,000

$80,000,000

$120,000,000

$160,000,000

$200,000,000

$240,000,000

$280,000,000

2004 2005 2006 2007

Baseline UC (assuming 10% annual growth in UC)Estimated UC, including additional UC from proposed changes

How Do These Estimated Increases in Uncompensated Care at Hospitals Compare to Current Levels?

+34%

+80%+88%

+63%

Lessons learned

Using state-specific data is important, but it likely can’t answer every question– State-specific: UC baseline data, uninsured

characteristics– Federal/national: MEPS, national studies

Can use both credibly, as long as their respective roles are appropriate

Use national data as crosscheck for state-specific data

Example 2

How will an aging population affect use of health care services and hospital bed capacity over the next 10, 20, and 30 years?

Very Brief Background on Example 2

Minnesota has operated under a hospital inpatient bed construction moratorium since 1984

Bed capacity essentially static for 20 yearsQuestion: how will population demographics

affect use of services and how will that compare to bed capacity?

Again: The need for both state and federal data

State: Demographic trends and projections, average length of stay

Federal: Hospitalization rates by age, average length of stay crosscheck

Projected Minnesota Population Growth,by Age Group

0% 20% 40% 60% 80% 100% 120%

2000-2030

2000-2020

2000-2010

60+40 to 5920 to 39Under 20

Source: Minnesota State Demographic Center

In Sheer Numbers, How Much Will Minnesota’s Elderly Population Increase?

594,266680,000

951,700

1,290,800

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

2000 2010 2020 2030

Source: Miinnesota State Demographic Center

How Does Use of Health Care Services Vary by Age? Hospitals

05

101520253035404550

<5yrs 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75yrs+ Allages

# ho

spita

lizat

ions

per

100

pop

ulat

ion

Sources: National Center for Health Statistics (2000 National HospitalDischarge Survey); U.S. Bureau of the Census

Baby boomers

Hospitalization Rates by Age (2000 data)

Projected Growth in Minnesota Hospital Utilization

11%

20%27%

16%

36%

60%

0%

10%

20%

30%

40%

50%

60%

70%

2000-2010 2000-2020 2000-2030

MN Population Inpatient Days

Source: Minnesota Department of Health, Health Economics Program

Sources of Growth in Projected Minnesota Hospital Utilization

Example: Inpatient Days

69.4%56.0%

45.4%

30.6%44.0%

54.6%

0%

20%

40%

60%

80%

100%

2000-2010 2000-2020 2000-2030

Population Growth Changing Age Distribution

Source: Minnesota Department of Health, Health Economics Program

Projections of Capacity Utilization (as % of total available MN hospital beds)

Baseline15%

increase15%

decrease

2000 57% 57% 57%

2010 66% 69% 62%

2020 77% 85% 69%

2030 91% 105% 78%

Source: Minnesota Department of Health, Health Economics Program

Lessons learned

Questions are sometimes less complicated than they seem

Relatively simple projections can give you estimates that are likely as accurate as expensive, sophisticated modeling– Tradeoff: timeliness and cost versus

perceived sophistication and credibility

Overall lessons learned and things to consider

Know what you can answer with state-specific data and what you can’t, and be prepared to support your decision

Know what to prepare for– CPS versus state-specific survey findings

How sophisticated does the analysis need to be?– Is it important it be an econometric model or does

simple projection get you just as close?– Cost/Timeliness/model understanding critical

Overall lessons learned and things to consider

Contracting with experts versus doing your own modeling/projection– Credibility?– There’s nothing magic or mystical about modeling;

understand assumptions and how the detail was arrived at

Use technical assistance– SHADAC, SCI, others

National data can be a critical and important crosscheck to state data